code: def __init__(self, input_dim, num_classes, args):
      super(Net, self).__init__()
      self.model = args.model
      if self.model == "GCN":
          self.conv1 = pyg_nn.GCNConv(input_dim, args.hidden_dim)
          self.conv2 = pyg_nn.GCNConv(args.hidden_dim, num_classes)
      elif self.model == "Spline":
          self.conv1 = pyg_nn.SplineConv(
              input_dim, args.hidden_dim, dim=1, kernel_size=2
          )
          self.conv2 = pyg_nn.SplineConv(
              args.hidden_dim, num_classes, dim=1, kernel_size=2
          )
      else:
          raise ValueError("unknown conv")
      self.loss_fn = torch.nn.BCEWithLogitsLoss()
  
imports: import sys
  import argparse
  from converter import *
  import copy
  import math
  import time
  import numpy
  from sklearn.metrics import *
  import torch
  import torch.nn.functional
  from torch.utils.data import DataLoader
  from torch_geometric.datasets import Planetoid
  from torch_geometric.datasets import TUDataset
  import torch_geometric.transforms
  import torch_geometric.nn
  from deepsnap.graph import Graph
  from deepsnap.dataset import GraphDataset
  from deepsnap.batch import Batch
  import pdb
label_desc: Use image node_id 1 to create a new Net object from inherited base classes: torch.nn with example: obj = Net(input_dim, num_classes, args)